Utility of perfusion imaging in acute stroke treatment: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Variability in imaging protocols and techniques has resulted in a lack of consensus regarding the incorporation of perfusion imaging into stroke triage and treatment. The objective of our study was to evaluate the available scientific evidence regarding the utility of perfusion imaging in determining treatment eligibility in patients with acute stroke and in predicting their clinical outcome. METHODS: We performed a systematic review of the literature using PubMed, Web of Science, and Cochrane Library focusing on themes of medical imaging, stroke, treatment, and outcome (CRD42016037817). We included randomized controlled trials, cohort studies, and case-controlled studies published from 2011 to 2016. Two independent reviewers conducted the study appraisal, data abstraction, and quality assessments of the studies. RESULTS: Our literature search yielded 13 studies that met our inclusion criteria. In total, 994 patients were treated with the aid of perfusion imaging compared with 1819 patients treated with standard care. In the intervention group 51.1% of patients had a favorable outcome at 3 months compared with 45.6% of patients in the control group (p=0.06). Subgroup analysis of studies that used multimodal therapy (IV tissue plasminogen activator, endovascular thrombectomy) showed a significant benefit of perfusion imaging (OR 1.89, 95% CI 1.43 to 2.51, p<0.01). CONCLUSIONS: Perfusion imaging may represent a complementary tool to standard radiographic assessment in enhancing patient selection for reperfusion therapy, with a subset of patients having up to 1.9 times the odds of achieving independent functional status at 3 months. This is particularly important as patients selected based on perfusion status often included individuals who did not meet the current treatment eligibility criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".